20 of 20 MLflow Jobs in the South East

or Azure) Ability to work independently and communicate effectively in a remote team Bonus Points Experience with Hugging Face Transformers , LangChain , or RAG pipelines Knowledge of MLOps tools (e.g. MLflow, Weights & Biases, Docker, Kubernetes) Exposure to data engineering or DevOps practices Contributions to open-source AI projects or research publications What We Offer Fully remote working A collaborative and inclusive More ❯

foundational LLMs to custom built cognitive agents, solving real world business challenges. Establishing scalable ML/AI pipelines and infrastructure, working closely with data engineering and DevOps functions (Kubernetes, MLflow, Vertex AI, etc.). Acting as a thought leader, identifying new AI use cases and communicating technical vision to executive stakeholders and cross-functional teams. Champion ethical AI principles, ensuring More ❯

Frameworks: PyTorch, TensorFlow, Hugging Face Transformers Bio-AI Tools: AlphaFold, RoseTTAFold, BioBERT, DeepCell, Cellpose Data Sources: Genomic datasets, microscopy images, biomedical literature Cloud & DevOps: AWS/GCP, Docker, Kubernetes, MLflow Languages: Python (essential), SQL, Bash Ideal Candidate Profile: 4+ years of experience in machine learning, with at least 2 years in biotech, healthcare, or life sciences Strong understanding of biological More ❯

Python and scripting for automation Solid understanding of cloud networking, security, and cross-cloud connectivity Experience in monitoring, observability, and cost optimisation Nice to Have Experience with ML tooling (MLflow, Kubeflow) Knowledge of FastAPI , Databricks, or Snowflake Exposure to SRE practices or cloud security certifications Familiarity with Prometheus , Grafana , or Datadog Interested? If you want to be part of a More ❯

learn, TensorFlow, PyTorch, or Hugging Face Solid understanding of machine learning fundamentals and performance evaluation techniques Experience working in cloud platforms (AWS, GCP, or Azure) with MLOps tools (e.g. MLflow, SageMaker, Vertex AI) Comfortable working independently and delivering high-quality work to tight timelines Experience working in fast-paced environments or scale-up settings Company Market leading financial services (fintech More ❯

relevant academic (research Masters level) and/or industry experience. Excellent knowledge and experience of managing an on-premise Kubenetes cluster. Excellent knowledge of Kubeflow and similar systems, e.g. MLflow Good programming ability in Python with familiarity with Linux systems including scripting and system configuration. Experience using AWS, e.g, Cognito, S3, EC2, Lamdas, etc. Experience with ML toolkits, e.g. PyTorch More ❯

including APIs, data structures, and async processing* Databricks/Microsoft Fabric* Cloud, preferably Azure (Data Lake, Functions, App Services)* Containerisation with Docker and CI/CD pipelines* MLOps tooling (MLFlow, Git-based versioning, environment tracking)Desirable Skills & Interests* LangChain, Langflow, or similar frameworks for building AI agents* LLMs or intelligent automation workflows* High-availability, scalable systems (microservices, event-based architectures More ❯

background in data engineering, analytics, or data science. Experience with modern data stacks (e.g., SQL, dbt, Airflow, Snowflake, Looker/Power BI) and AI/ML tooling (e.g., Python, MLflow, MLOps). A track record of building and managing high-performing data teams. Strategic thinking and ability to influence senior stakeholders, comfortable in dual-regulated environments. HOW TO APPLY: Please More ❯

Experience Academic background (research Masters level) or industry experience in a relevant field Strong experience managing on premise Kubernetes clusters Deep knowledge of Kubeflow or similar systems such as MLflow Proficient in Python and experienced with Linux systems Familiar with AWS services such as Cognito, S3, EC2 and Lambda Experience working with ML frameworks such as PyTorch or Lightning Capable More ❯

Experience Academic background (research Masters level) or industry experience in a relevant field Strong experience managing on premise Kubernetes clusters Deep knowledge of Kubeflow or similar systems such as MLflow Proficient in Python and experienced with Linux systems Familiar with AWS services such as Cognito, S3, EC2 and Lambda Experience working with ML frameworks such as PyTorch or Lightning Capable More ❯

call expectation outside of core office hours . Key Responsibilities: Manage and maintain on-premise Kubernetes clusters Implement and maintain ML Ops pipelines using Kubeflow and similar tools (e.g., MLflow) Develop scripts and tooling in Python; manage Linux system configurations Leverage AWS infrastructure (Cognito, S3, EC2, Lambda, etc.) Integrate ML toolkits (e.g., PyTorch, Lightning) into ML Ops workflows Design and More ❯

call expectation outside of core office hours . Key Responsibilities: Manage and maintain on-premise Kubernetes clusters Implement and maintain ML Ops pipelines using Kubeflow and similar tools (e.g., MLflow) Develop scripts and tooling in Python; manage Linux system configurations Leverage AWS infrastructure (Cognito, S3, EC2, Lambda, etc.) Integrate ML toolkits (e.g., PyTorch, Lightning) into ML Ops workflows Design and More ❯

native environments Strong technical background in data engineering, analytics, or science Experience working with Finance and Risk teams Familiar with AWS and modern data/AI stacks (SQL, Python, MLflow, MLOps) Strategic mindset with ability to scale teams and influence at C-level More ❯

backend development. Solid understanding of data processing and engineering workflows. Experience building APIs or services to support data or ML applications. Familiarity with ML model lifecycle and tooling (e.g. MLflow, Airflow, Docker). Strong problem-solving skills and the ability to work autonomously in a dynamic environment. DESIRABLE SKILLS Experience supporting LLM training or retrieval-augmented generation (RAG). Familiarity More ❯

research Masters level) and/or industry experience. Essential Skills Excellent knowledge and experience of managing an on-premise Kubenetes cluster. Excellent knowledge of Kubeflow and similar systems, e.g. MLflow Good programming ability in Python with familiarity with Linux systems including scripting and system configuration. Experience using AWS, e.g, Cognito, S3, EC2, Lamdas, etc. Experience with ML toolkits, e.g. PyTorch More ❯

both on-prem, self-managed systems and also leverage AWS infrastructure. Essential: Experience of managing an on-prem Kubernetes clusters. Working knowledge of Kubeflow and other systems such as MLflow Exposure to cloud based systems e.g. AWS, Azure or GCP (they use AWS) Knowledge of Linux systems including scripting and system configuration. Understanding of how ML tools fit into MLOps More ❯

Cloud services (Data Lake, Functions, App Services) for development and deployment Implement and maintain containerized solutions using Docker and CI/CD pipelines Apply MLOps practices using tools like MLFlow , Git-based versioning, and environment tracking Build and integrate AI and intelligent automation frameworks Ensure applications meet performance, reliability, and scalability requirements Required Skills & Experience: 2+ years of Python development … Proficiency in Azure cloud services including Data Lake, Functions, and App Services Strong understanding of Docker , CI/CD workflows, and automation pipelines Familiarity with MLOps tooling such as MLFlow, Git version control, and environment management Desirable Skills & Interests: Experience with frameworks like LangChain , Langflow , or similar tools for building AI agents Understanding of Large Language Models (LLMs) and intelligent More ❯

across the Azure stack. Your responsibilities will include: Designing and deploying ETL pipelines using PySpark and Delta Lake on Databricks. Supporting the deployment and operationalisation of ML models with MLflow and Databricks Workflows. Building out reusable data products and feature stores for data science teams. Tuning performance across clusters, jobs, and workflows. Migrating legacy systems (SSIS/SQL) to Databricks … Strong background in data engineering and distributed processing . Hands-on knowledge of Azure Data Lake , Data Factory , or similar orchestration tools. Experience with ML model deployment , preferably using MLflow or similar tools. Proficient in SQL , Python , and cloud-based data pipelines. Comfortable in fast-paced, agile delivery environments. DESIRABLE SKILLS Experience building or integrating feature stores . Familiarity with More ❯

Machine Learning Engineer (SC Cleared) London (Hybrid) 2 Month Contract £550/day (Inside IR35) Machine Learning Engineer needed with active SC Security Clearance, plus strong Databricks, MLFlow and MLOps experience. The ideal candidate will have a strong background in Machine Learning (ML) Engineering and in-depth expertise in operationalising models in Databricks, MLFlow and MLOps environments. A chance to More ❯

Machine Learning Engineer (SC Cleared) London (Hybrid) 2 Month Contract £550/day (Inside IR35) Machine Learning Engineer needed with active SC Security Clearance, plus strong Databricks, MLFlow and MLOps experience. The ideal candidate will have a strong background in Machine Learning (ML) Engineering and in-depth expertise in operationalising models in Databricks, MLFlow and MLOps environments. A chance to More ❯